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 },
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 "cells": [
  {
   "source": [
    "# BP神经网络分类心电信号"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as Fun"
   ]
  },
  {
   "source": [
    "## 读取数据并处理"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_filepath = \"../data/numpy_data/\"\n",
    "\n",
    "X_train = np.load(data_filepath + 'X_train.npy')\n",
    "y_train = np.load(data_filepath + 'y_train.npy', allow_pickle=True)\n",
    "X_test = np.load(data_filepath + 'X_test.npy')\n",
    "y_test = np.load(data_filepath + 'y_test.npy', allow_pickle=True)\n",
    "\n",
    "# reshape y_train, y_test\n",
    "y_train = y_train.reshape(len(y_train), 1)\n",
    "y_test = y_test.reshape(len(y_test), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shuffle_train_test(X_train, y_train, X_test, y_test):\n",
    "    shuffled_indices_x = np.random.permutation(len(y_train))\n",
    "    shuffled_indices_y = np.random.permutation(len(y_test))\n",
    "\n",
    "    return X_train[shuffled_indices_x], y_train[shuffled_indices_x], X_test[shuffled_indices_y], y_test[shuffled_indices_y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 打乱数据\n",
    "X_train, y_train, X_test, y_test = shuffle_train_test(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "source": [
    "### 调整数据格式"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "num2class = np.array(['NORM', 'MI', 'STTC', 'CD', 'HYP'])\n",
    "\n",
    "for index in range(len(y_train)):\n",
    "    y_train[index] = np.where(num2class == y_train[index][0][0])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index in range(len(y_test)):\n",
    "    y_test[index] = np.where(num2class == y_test[index][0][0])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_train = y_train.reshape(1, -1)[0].astype('uint8')\n",
    "# y_test = y_test.reshape(1, -1)[0].astype('uint8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.astype('uint8')\n",
    "y_test = y_test.astype('uint8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[4],\n",
       "       [2],\n",
       "       [2],\n",
       "       ...,\n",
       "       [3],\n",
       "       [0],\n",
       "       [0]], dtype=uint8)"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[3],\n",
       "       [0],\n",
       "       [0],\n",
       "       ...,\n",
       "       [0],\n",
       "       [0],\n",
       "       [1]], dtype=uint8)"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "source": [
    "### label转换为one-hot"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 1, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       ...,\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0]])"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "encoder = LabelBinarizer()\n",
    "y_train_oneHot = encoder.fit_transform(y_train)\n",
    "y_train_oneHot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 1],\n",
       "       [0, 0, 1, 0, 0],\n",
       "       [0, 0, 1, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, 1, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0]])"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "y_test_oneHot = encoder.fit_transform(y_test)\n",
    "y_test_oneHot"
   ]
  },
  {
   "source": [
    "## PCA降维"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "X_train_flatten = X_train.reshape(len(X_train), 6000)\n",
    "pca_train = PCA(n_components=180)\n",
    "pca_train.fit(X_train_flatten)\n",
    "\n",
    "X_train_pca = pca_train.fit_transform(X_train_flatten)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.9796348833217096"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "pca_train.explained_variance_ratio_.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(16966, 180)"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "X_train_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_flatten = X_test.reshape(len(X_test), 6000)\n",
    "pca_test = PCA(n_components=180)\n",
    "pca_test = PCA(X_test_flatten)\n",
    "\n",
    "X_test_pca = pca_train.fit_transform(X_test_flatten)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_pca = pca_train.fit_transform(X_test_flatten)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(1901, 180)"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "X_test_pca.shape"
   ]
  },
  {
   "source": [
    "### 归一化处理"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train_normalization = (X_train_pca - np.mean(X_train_pca, axis=1)) / np.std(X_train_pca, axis=1)\n",
    "# X_test_normalization = (X_test_pca - np.mean(X_test_pca, axis=1)) / np.std(X_test_pca, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train_normalization[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %matplotlib auto\n",
    "# ecgPca = pca_train.inverse_transform(X_train_normalization[0]).reshape(500, 12)\n",
    "# plt.figure()\n",
    "# for index in range(12):\n",
    "#     plt.subplot(6, 2, index+1)\n",
    "#     plt.plot(ecgPca[:,index])\n",
    "# plt.show()"
   ]
  },
  {
   "source": [
    "### 将`numpy`转换为`tensor`"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_tensor = torch.tensor(X_train_pca, dtype = torch.float32)\n",
    "y_train_tensor = torch.tensor(y_train, dtype = torch.float32)\n",
    "\n",
    "X_test_tensor = torch.tensor(X_test_pca, dtype = torch.float32)\n",
    "y_test_tensor = torch.tensor(y_test, dtype = torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 合并打包，方便后面使用mini-batch的方法\n",
    "dataset = torch.utils.data.TensorDataset(X_train_tensor, y_train_tensor)\n",
    "train_iter = torch.utils.data.DataLoader(dataset, 50 ,shuffle=True)"
   ]
  },
  {
   "source": [
    "## BP神经网络模型\n",
    "\n",
    "使用简单的三层BP神经网络实现心电的分类，这里容易得到的是神经网络输入层根据输入变量`X`维度，需要设置180个神经元， 输出层因为是5分类，需要5个输出层，中间的隐含层根据公式\n",
    "\n",
    "$$\n",
    "n_1 = \\sqrt{m+n} + a\n",
    "$$\n",
    "\n",
    "设置隐含层神经元数在15-25之间"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as Fun\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else\n",
    "\"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(torch.nn.Module):\n",
    "    def __init__(self, n_features, n_hidden, n_output):\n",
    "        super(Net, self).__init__()\n",
    "        self.hidden = torch.nn.Linear(n_features, n_hidden)\n",
    "        self.out = torch.nn.Linear(n_hidden, n_output)\n",
    "\n",
    "    def forward(self, X_data):\n",
    "        x = self.hidden(X_data)\n",
    "        x_relu = Fun.tanh(x)\n",
    "        x = self.out(x_relu)\n",
    "        predict_y = Fun.softmax(x)\n",
    "        return predict_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_layers = 20\n",
    "net = Net(180, hidden_layers, 5)\n",
    "net.cuda()\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.0001)\n",
    "loss = torch.nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "----epoches 0 finished----\n"
     ]
    }
   ],
   "source": [
    "train_ls, test_ls = [], []\n",
    "epoches = 100\n",
    "X_test_tensor = X_test_tensor.to(device)\n",
    "y_test_tensor = y_test_tensor.to(device)\n",
    "for epoch in range(epoches):\n",
    "    if epoch/10 == 0:\n",
    "        print(\"----epoches %d finished----\" % epoch)\n",
    "    for x, y in train_iter:\n",
    "        x = x.to(device)\n",
    "        y = y.to(device)\n",
    "        out = net(x)\n",
    "        # print(out)\n",
    "        # print(y.reshape(1,-1)[0])\n",
    "        l = loss(out, y.reshape(1,-1)[0].long())\n",
    "        optimizer.zero_grad()\n",
    "        l.backward()\n",
    "        optimizer.step()\n",
    "    train_ls.append(l)\n",
    "    if test_ls is not None:\n",
    "        test_ls.append(loss(net(X_test_tensor), y_test_tensor.reshape(1,-1)[0].long()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 绘制训练误差\n",
    "plt.plot(train_ls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 绘制测试误差\n",
    "plt.plot(test_ls, c='r')\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## 预测测试集验证性能"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(1901, 5)"
      ]
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "source": [
    "y_test_pred = net(X_test_tensor).detach_().cpu().numpy()\n",
    "y_test_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred_label = y_test_pred.argmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(1901,)"
      ]
     },
     "metadata": {},
     "execution_count": 32
    }
   ],
   "source": [
    "y_test_pred_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[449,  30, 120, 169,  34],\n",
       "       [ 87,  20,  28,  81,   6],\n",
       "       [102,  35,  27,  97,   4],\n",
       "       [123,  40,  43, 220,  24],\n",
       "       [ 74,   6,  12,  55,  15]], dtype=int64)"
      ]
     },
     "metadata": {},
     "execution_count": 33
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,precision_score, recall_score, f1_score\n",
    "\n",
    "confusion_matrix(y_test, y_test_pred_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.38453445554971066"
      ]
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "precision_score(y_test, y_test_pred_label, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.38453445554971066"
      ]
     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "source": [
    "recall_score(y_test, y_test_pred_label, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.38453445554971066"
      ]
     },
     "metadata": {},
     "execution_count": 36
    }
   ],
   "source": [
    "f1_score(y_test, y_test_pred_label, average='micro')"
   ]
  },
  {
   "source": [
    "## 尝试二分类"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([[0.],\n",
       "        [1.],\n",
       "        [1.],\n",
       "        ...,\n",
       "        [1.],\n",
       "        [1.],\n",
       "        [0.]])"
      ]
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "source": [
    "y_train_tensor_norm = (y_train_tensor == 0).float()\n",
    "y_train_tensor_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        ...,\n",
       "        [0.],\n",
       "        [1.],\n",
       "        [1.]], device='cuda:0')"
      ]
     },
     "metadata": {},
     "execution_count": 53
    }
   ],
   "source": [
    "y_test_tensor_norm = (y_test_tensor == 0).float()\n",
    "y_test_tensor_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_norm = torch.utils.data.TensorDataset(X_train_tensor.to(device), y_train_tensor_norm.to(device))\n",
    "train_iter_norm = torch.utils.data.DataLoader(dataset_norm, 5 ,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(torch.nn.Module):\n",
    "    def __init__(self, n_features, n_hidden, n_output):\n",
    "        super(Net, self).__init__()\n",
    "        self.hidden = torch.nn.Linear(n_features, n_hidden)\n",
    "        self.out = torch.nn.Linear(n_hidden, n_output)\n",
    "\n",
    "    def forward(self, X_data):\n",
    "        x = self.hidden(X_data)\n",
    "        x_relu = Fun.tanh(x)\n",
    "        x = self.out(x_relu)\n",
    "        predict_y = Fun.sigmoid(x)\n",
    "        return predict_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_layers = 20\n",
    "net = Net(180, hidden_layers, 1)\n",
    "net.cuda()\n",
    "optimizer = torch.optim.SGD(net.parameters(), lr=0.0001)\n",
    "loss = torch.nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "----epoches 0 finished----\n",
      "----epoches 10 finished----\n",
      "----epoches 20 finished----\n",
      "----epoches 30 finished----\n",
      "----epoches 40 finished----\n",
      "----training success----\n"
     ]
    }
   ],
   "source": [
    "train_ls_norm, test_ls_norm = [], []\n",
    "epoches = 50\n",
    "X_test_tensor_norm = X_test_tensor.to(device)\n",
    "y_test_tensor_norm = y_test_tensor.to(device)\n",
    "for epoch in range(epoches):\n",
    "    if epoch % 10 == 0:\n",
    "        print(\"----epoches %d finished----\" % epoch)\n",
    "    for x, y in train_iter_norm:\n",
    "        # x = x.to(device)\n",
    "        # y = y.to(device)\n",
    "        out = net(x)\n",
    "        # print(out.shape)\n",
    "        # print(y)\n",
    "        l = loss(out, y)\n",
    "        optimizer.zero_grad()\n",
    "        l.backward()\n",
    "        optimizer.step()\n",
    "    train_ls_norm.append(l)\n",
    "    if test_ls_norm is not None:\n",
    "        \n",
    "        test_ls_norm.append(loss(net(X_test_tensor), y_test_tensor.reshape(1,-1)[0].long()))\n",
    "print(\"----training success----\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(train_ls_norm)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "metadata": {},
     "execution_count": 44
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(test_ls_norm)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [0],\n",
       "       [1],\n",
       "       ...,\n",
       "       [1],\n",
       "       [0],\n",
       "       [1]], dtype=uint8)"
      ]
     },
     "metadata": {},
     "execution_count": 45
    }
   ],
   "source": [
    "y_test_pred_norm = net(X_test_tensor_norm).detach_().cpu().numpy()\n",
    "y_test_pred_norm = (y_test_pred_norm >= 0.5).astype('uint8')\n",
    "y_test_pred_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        ...,\n",
       "        [0.],\n",
       "        [1.],\n",
       "        [1.]], device='cuda:0')"
      ]
     },
     "metadata": {},
     "execution_count": 54
    }
   ],
   "source": [
    "y_test_tensor_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.5698456166775066"
      ]
     },
     "metadata": {},
     "execution_count": 55
    }
   ],
   "source": [
    "precision_score(y_test_tensor_norm.cpu().numpy().reshape(1, -1)[0].astype('uint8'), y_test_pred_norm.reshape(1,-1)[0].astype('uint8'), average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.5694487620802406"
      ]
     },
     "metadata": {},
     "execution_count": 56
    }
   ],
   "source": [
    "recall_score(y_test_tensor_norm.cpu().numpy().reshape(1, -1)[0].astype('uint8'), y_test_pred_norm.reshape(1,-1)[0].astype('uint8'), average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.5696051460934425"
      ]
     },
     "metadata": {},
     "execution_count": 57
    }
   ],
   "source": [
    "f1_score(y_test_tensor_norm.cpu().numpy().reshape(1, -1)[0].astype('uint8'), y_test_pred_norm.reshape(1,-1)[0].astype('uint8'), average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}